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5,141 results for “Education”
Preparing educational presentation about therapeutic exercises to resident physiatrists
<p>This study evaluated the effectiveness of ChatGPT-4o, a generative artificial intelligence (AI) platform, in preparing educational presentation on therapeutic exercises specifically designed for physiatry residents. Both a physiatry expert and ChatGPT-4 created PowerPoint slides for a presentation on therapeutic exercises using the same reputable sources. Two other physiatry experts, blinded to the origin of the presentation and each other's scores, independently assessed both presentations using four of the CLEAR criteria (completeness, lack of false information, appropriateness, and relevance).</p> <p>Statistical analyses confirmed the interrater reliability. The average scores of the expert-prepared slides were significantly higher than those of the AI-prepared slides. However, when assessing the presentations as a whole, no statistically significant difference emerged between the average AI and expert scores. The overall scores of the AI-prepared slides were rated between good and excellent, while the expert-prepared slides were rated as excellent. Similarly, when assessing the presentations as a whole, the AI performed good whereas expert performed excellent. Upon evaluating the variability within the model for the assessed criteria, it was found that the AI ranked highest in relevance, whereas the expert ranked highest in terms of lack of false information.</p> <p>These findings indicated that although ChatGPT-4o can produce effective educational content, the expert still outperformed AI. This underscores the importance of professional oversight in maintaining the educational quality of resident physician training. Furthermore, fostering collaboration between humans and AI can lead to enhanced educational outcomes in the field.</p>
Software Engineering Education Knowledge versus Industrial Needs
<p>Dataset of the research paper: <strong>Software Engineering Education Knowledge versus Industrial Needs</strong></p> <p><em>Contribution</em>: Determine and analyze the gap between software practitioners’ education outlined in the 2014 IEEE/ACM Software Engineering Education Knowledge (SEEK) and industrial needs pointed by Wikipedia articles referenced in Stack Overflow (SO) posts.<br> <em>Background</em>: Previous work has uncovered deficiencies in the coverage of computer fundamentals, people skills, software processes, and human-computer interaction, suggesting rebalancing.<br> <em>Research Questions</em>: 1) To what extent are developers’ needs, in terms of Wikipedia articles referenced in SO posts, covered by the SEEK knowledge units? 2) How does the popularity of Wikipedia articles relate to their SEEK coverage? 3) What areas of computing knowledge can be better covered by the SEEK knowledge units? 4) Why are Wikipedia articles covered by the SEEK knowledge units cited on SO?<br> <em>Methodology</em>: Wikipedia articles were systematically collected from SO posts. The most cited were manually mapped to the SEEK knowledge units, assessed according to their degree of coverage. Articles insufficiently covered by the SEEK were classified by hand using the 2012 ACM Computing Classification System. A sample of posts referencing sufficiently covered articles was manually analyzed. A survey was conducted on software practitioners to validate the study findings.<br> <em>Findings</em>: SEEK appears to cover sufficiently computer science fundamentals, software design and mathematical concepts, but less so areas like the World Wide Web, software engineering components, and computer graphics. Developers seek advice, best practices and explanations about software topics, and code review assistance. Future SEEK models and the computing education could dive deeper in information systems, design, testing, security, and soft skills.</p> <p>The following data files are included.</p> <ul> <li><strong>wikipedia_articles.csv</strong>: Wikipedia articles mapped to the knowledge units of the 2014 IEEE/ACM Software Engineering Education Knowledge (SEEK) and the first and second level categories of the 2012 ACM Computing Classification System (CCS).</li> <li> <p><strong>posts_analysis.csv</strong>: Stack Overflow post data and metadata.</p> </li> <li> <p><strong>posts_aggregated_codes.csv</strong>: The aggregated codes that resulted from the manual analysis of the Stack Overflow posts by grouping individual keywords assigned to the posts.</p> </li> <li> <p><strong>survey_questionnaire.csv</strong>: The final survey questionnaire.</p> </li> <li> <p><strong>survey_responses.csv</strong>: Anonymized responses of the final survey questionnaire. (E-mail addresses have been excluded for privacy reasons.)</p> </li> </ul>
Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies
<p>These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684</p>
Data and code for "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"
<p>Code and data for the paper "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"</p> <p>File descriptions:</p> <p>'HEI_analysis_OneEarth.Rmd' is the code with improved annotation and colorblind-friendly figures.</p> <p>All other data files are provided as excel and csv for convenience.</p> <p>'working_master_data' contains data from the Second Nature reporting platform on emissions by category for each institution analyzed in the paper (measured in metric tons). All adjustments necessary to fill in the data gaps in this file are documented at the beginning of 'HEI_analysis'.</p> <p>'offsets' contains data on the type(s) of offsets purchased by each school in their carbon neutral year (measured in metric tons). This data was assembled from a variety of sources which are documented at the beginning of 'HEI_analysis'.</p> <p>'carbon_neutral_years' contains yearly counts of higher education neutrality goals that were reported to Second Nature as of November 2020.</p>
Sustaining Cities, Naturally Webinar: Education Session - Breathe Respirar Programme - CONEXUS schools
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Sustaining Cities, Naturally Webinar: Education Session - Vigie-Nature école
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Chinese Educational Mission Dataset (1872-1881)
<p>This series of 11 datasets is drawn from Rhoads, Edward J. M. <em>Stepping Forth into the World: The Chinese Educational Mission to the United States, 1872-81</em>. Hong Kong University Press, 2011.</p> <p>They document the 120 young Chinese who participated in the pioneering Chinese Educational Mission (CEM) in the United States (1872-1881). The first 8 files are drawn directly from the tables in Rhoads: </p> <ol> <li>Table 2.1 CEM students, by detachment (p.14-17)</li> <li>Table 5.1. Initial host family assignments (p.51-54)</li> <li>Table 7.1. CEM students in middle schools (by state and locality) (p. 90-94)</li> <li>Table 7.2 CEM students in public high schools (by state and locality) (p.96-99)</li> <li>Table 7.3 CEM students in private academies (by state and locality) (p.99-100)</li> <li>Table 8.1 CEM students in colleges (by academic year of enrollment) (p.116-118)</li> <li>Table 9.1 Deaths, dismissals, and withdrawals from the CEM (by date) (p.136)</li> <li>Table 9.2 CEM students in the June 1880 census (p.138-142)</li> </ol> <p>Based on these tables, I created three synthetic datasets which can be used for statistical and network analyses: </p> <ol> <li>cem_attributes: students' vital attributes, including their multiple names and transliteration, date and place of birth, and other attribute data (one row for each individual). </li> <li>cem_host: students' host families in the United States</li> <li>cem_education: students' educational curricula </li> </ol> <p>Each file contains two tabs, one for the data (data), one for the description of variables (key). Grey columns refer to the unstructured information given in the original source. </p>
Umfragedaten: Studentische Perspektiven auf Open Educational Resources in der Rechtswissenschaft
<p>Der Datensatz enthält die Rohdaten, ausgewerteten Daten und das dazugehörige R-Skript für eine 2022 durchgeführte Umfrage unter Studierenden der Rechtswissenschaft in Deutschland zu Chancen und Grenzen von Open Educational Resources. Für weitere Fragen zu der Umfrage wenden Sie sich bitte an <a href="https://www.jura.uni-muenster.de/de/institute/lehrstuhl-fuer-oeffentliches-recht-voelker-und-europarecht-sowie-empirische-rechtsforschung/team/weitere-personen/max-milas/">Max Milas</a> oder <a href="https://www.jura.fu-berlin.de/fachbereich/einrichtungen/oeffentliches-recht/lehrende/calliessc/Mitarbeiterinnen-und-Mitarbeiter/Wissenschaftliche_Mitarbeiter_innen/Valentina-Chiofalo/index.html">Valentina Chiofalo</a>. </p>
Green Intelligence Awareness, educational actions in Valladolid city
<p>Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population.</p> <p>There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness. </p>
Database of fields of education, with explanatory note
<p>In addition to respondents’ highest educational qualification, some surveys also collect data on their main field of education. Current measurement practice involves either a closed question with highly aggregated response categories, which are difficult to use for respondents, or an open question, requiring expensive post-coding. Therefore, a measurement tool for fields of education was developed in the SERISS-project in work package 8, Task 8.3. In deliverable D8.9 we provide a database of fields of education and training in 34 languages, including the definition of a search tree interface to facilitate navigation of categories for respondents. All 120 standard categories and classification codes are taken from UNESCO's International Standard Classification of Education for Fields of Education and Training (ISCED-F). For most languages, detailed 3-digit information is available. The database, including a live search feature, is available at the surveycodings website at https://surveycodings.org/articles/codings/fields-of-education. The search tree can be used for respondents’ self-identification of fields of education and training in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data. </p>
Database of educational attainment, with explanatory note
<p>Most social surveys collect data on respondents’ educational attainment. Current measurement practice involves a closed question with country-specific response options, which are needed because of the differences between educational systems. However, these are quite difficult to compare across countries. This is a challenge for both migrant and international surveys. Therefore, a measurement tool for educational attainment that was initially developed for German migrant surveys in the CAMCES project (Schneider, Briceno-Rosas, Herzing, et al. 2018; Schneider, Briceno-Rosas, Ortmanns, et al. 2018) was extended in the SERISS-project in work package 8, Task 8.3. In deliverable D8.8, we provide a database of educational qualifications and levels for 100 countries, including the definition of a search tree interface to facilitate the navigation of categories for respondents in computer-assisted surveys. All country-specific categories are linked to 3-digit codes of UNESCO's International Standard Classification of Education 2011 for Educational Attainment (ISCED-A), as well as to the education coding scheme used in the European Social Survey (ESS), "edulvlb". A live search of the database via two different interfaces, a search box (for a limited set of countries) and a search tree (for all countries), is available at the surveycodings website at https://surveycodings.org/articles/codings/levels-of-education. The search box and search tree can be implemented in survey questionnaires and thereby be used for respondents’ self-classification in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data. </p>
Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform
<p>Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform<br> =======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7912941</p> <p><br> • The corresponding article : El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p> <p><br> • License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date: May 9th 2023</p> <p>• Subject: Educational change, Scalability, Professional Development, Digital Education, Curricular<br> Reform, Primary School</p> <p>• Dataset format: CSV</p> <p>• Dataset collection: September 2018 to September 2022</p> <p>• Dataset size : < 100 kB</p> <p>• Dataset content : one excel file with detailed description below. Please note that the spreadsheet may contain missing values due to teachers either choosing not to respond to the questions or the questions not being presented at each of the training sessions. To have access to the specific survey questions please refer to the associated publication [a].</p> <p>• Abbreviations :<br> - DE : Digital Education<br> - PD : Professional Development</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p># References</p> <p>[a] El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p>
Dataset Worldwide Survey on the Impact of AI Chatbots and Large Language Models in Dental Education: Insights from Dental Educators
<p><strong>This dataset contains responses from participants regarding their awareness, knowledge, and perceptions of AI-powered tools in dental education. The data was collected during May-June 2023 to investigate the potential enhancement that AI can bring to dental education. The dataset includes variables related to participants' demographics, experiences, perceptions, and opinions.</strong></p> <p><strong>Details in the published protocol by Uribe, S. E., & Maldupa, I. (2023, June 2). Chatbots In Dental Education - Research Protocol. https://doi.org/10.17605/OSF.IO/3BSG2</strong></p>
Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials
<p>Data extraction table for scoping review on best practices for co-creating patient-facing educational materials</p>
Higher Education Institutions in Poland Dataset
<p><strong>Higher Education Institutions in Poland Dataset</strong></p> <p>This repository contains a dataset of higher education institutions in Poland. The dataset comprises 131 public higher education institutions and 216 private higher education institutions in Poland. The data was collected on 24/11/2022. <br> This dataset was compiled in response to a cybersecurity investigation of Poland's higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].</p> <p><strong>Data</strong></p> <p>The data includes the following fields for each institution:</p> <ul> <li>Id: A unique identifier assigned to each institution.</li> <li>Region: The federal state in which the institution is located.</li> <li>Name: The original name of the institution in Polish.</li> <li>Name_EN: The international name of the institution in English.</li> <li>Category: Indicates whether the institution is public or private.</li> <li>Url: The website of the institution.</li> </ul> <p><strong>Methodology</strong></p> <p>The dataset was compiled using data from two primary sources:</p> <ul> <li>Public Higher Education Institutions: Data was sourced from the official website of the Ministry of Education and Science of Poland [3].</li> <li>Private Higher Education Institutions: Data was obtained from the RAD-on system, which is part of the Integrated Information Network on Science and Higher Education [4].</li> </ul> <p>For the international names in English, the following methodology was employed:</p> <p>Both Polish and English names were retained for each institution. This decision was based on the fact that some universities do not have their English versions available in official sources.</p> <p>English names were primarily sourced from:</p> <ul> <li>The Polish National Agency for Academic Exchange's official document [5].</li> <li>The website Studies in English [6].</li> <li>Official websites of the respective Higher Education Institutions.</li> </ul> <p>In instances where English names were not readily available from the aforementioned sources, the GPT-3.5 model was employed to propose suitable names. These proposed names are distinctly marked in blue within the dataset file (hei_poland_en.xls).</p> <p><strong>Usage</strong></p> <p>This data is available under the Creative Commons Zero (CC0) license and can be used for academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].</p> <p>If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI:<br> <strong>10.5281/zenodo.8333573</strong></p> <p><strong>Contribution</strong></p> <p>If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.</p> <p><strong>Acknowledgment</strong></p> <p>We would like to express our gratitude to the Ministry of Education and Science of Poland and the RAD-on system for providing the information used in this dataset.</p> <p>We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Polytechnic University of Viana do Castelo, Portugal.</p> <p><strong>References</strong></p> <ol> <li>Pending.</li> <li>S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [<a href="https://doi.org/10.5281/zenodo.1212496">https://doi.org/10.5281/zenodo.1212496</a>]</li> <li>Ministry of Education and Science of Poland. "Wykaz uczelni publicznych nadzorowanych przez Ministra właściwego ds. szkolnictwa wyższego - publiczne uczelnie akademickie." Nov 2022. [Online]. Available: <a href="https://www.gov.pl/web/edukacja-i-nauka/wykaz-uczelni-publicznych-nadzorowanych-przez-ministra-wlasciwego-ds-szkolnictwa-wyzszego-publiczne-uczelnie-akademickie">https://www.gov.pl/web/edukacja-i-nauka/wykaz-uczelni-publicznych-nadzorowanych-przez-ministra-wlasciwego-ds-szkolnictwa-wyzszego-publiczne-uczelnie-akademickie</a></li> <li>RAD-on System. "Dane instytucji systemu szkolnictwa wyższego i nauki." Nov 2022. [Online]. Available: <a href="https://radon.nauka.gov.pl/dane/instytucje-systemu-szkolnictwa-wyzszego-i-nauki">https://radon.nauka.gov.pl/dane/instytucje-systemu-szkolnictwa-wyzszego-i-nauki</a></li> <li>Polish National Agency for Academic Exchange. "List of the university-type HEIs." 2023. [Online]. Available: <a href="https://nawa.gov.pl/images/Aktualnosci/2023/Att.-2.-List-of-the-university-type-HEIs.pdf">https://nawa.gov.pl/images/Aktualnosci/2023/Att.-2.-List-of-the-university-type-HEIs.pdf</a></li> <li>Studies in English. [Online]. Available: <a href="http://www.studies-in-english.pl/">www.studies-in-english.pl</a></li> </ol>
PL12 - Educational - Warszawa (Poland)
<p>Data files for building: PL12 - Educational - Warszawa (Poland)</p><p>Languages: Polish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
PL05 - Educational - Mińsk Mazowiecki (Poland)
<p>Data files for building: PL05 - Educational - Mińsk Mazowiecki (Poland)</p><p>Languages: Polish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
PL01 - Educational - Widawa (Poland)
<p>Data files for building: PL01 - Educational - Widawa (Poland)</p><p>Languages: Polish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
GR11 - Educational - Kastoria (Greece)
<p>Data files for building: GR11 - Educational - Kastoria (Greece)</p><p>Languages: Greek, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
PL06 - Educational - Zielonka (Poland)
<p>Data files for building: PL06 - Educational - Zielonka (Poland)</p><p>Languages: Polish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.